Difference between revisions of "Group17 proposal"
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The growing menace of [https://en.wikipedia.org/wiki/Terrorism Terrorism] has been a major issue in many parts of the world. An attack of terrorism in a country impacts its economy and the livelihood of the people impacted by the attack tremendously. The global death toll from terrorism over the past decade has increased by almost 5 times. There has been an average of over 21,000 people getting killed every year worldwide because of terrorism. Because of the growing concern of terrorism across the world in the past decade, we decided to choose this topic for the project. The scope of this project is to understand how the landscape of terrorism and various terrorist activities varies across the world and has changed over time. | The growing menace of [https://en.wikipedia.org/wiki/Terrorism Terrorism] has been a major issue in many parts of the world. An attack of terrorism in a country impacts its economy and the livelihood of the people impacted by the attack tremendously. The global death toll from terrorism over the past decade has increased by almost 5 times. There has been an average of over 21,000 people getting killed every year worldwide because of terrorism. Because of the growing concern of terrorism across the world in the past decade, we decided to choose this topic for the project. The scope of this project is to understand how the landscape of terrorism and various terrorist activities varies across the world and has changed over time. | ||
| − | == Project | + | == Project Motivation== |
| − | + | <p> With the recent emergence of Covid virus, containing the epidemic requires an understanding of how corona virus spreads, and factors impacting the intensity of cases within and across regions. This project aims at delivering an R shiny app that first provides a basic understanding of the nature of the virus, e.g. the types of pathogens identified in MERS, the kinds of organisms which are susceptible to MERS contraction, and time series analysis to visualize the evolution of MERS outbreak across a 6-years time period (2012-2018). The detailed description of these variables are shown in the Section:Data Description. Geospatio-temporal analysis will be performed to identify the intensity of outbreak in different regions across time. Finally, we will further deep-dive into how certain factors intensifies the spread of the disease using spatial-join analysis. </p> | |
| − | < | + | == Proposed Analytical Methods & Visualisation == |
| + | <b> 1. Exploratory</b> | ||
| + | Radar Chart on Pathogen Types | ||
| − | + | Line Chart (Trellis): | |
| + | To visualise the number of MERS cases over time | ||
| + | The line chart will be partitioned by organism_type variable (records the type of organism on which MERS was tested positive) | ||
| + | |||
| + | Slope Chart | ||
| + | Compares the ranking/ intensity of MERS incidents in each region over time. | ||
| + | Reference: https://www.r-bloggers.com/creating-slopegraphs-with-r/ | ||
| + | |||
| + | <b> 2. Spatio-Temporal Analysis </b> | ||
| + | (Describe point pattern analysis here) | ||
| + | Kernel Density Plot | ||
| + | 3D Chart (latitude, longitude and time) | ||
| + | |||
| + | <b> 3. Spatial Join Analysis </b> | ||
| + | Visualisations TBC | ||
== Project Timeline == | == Project Timeline == | ||
| − | |||
== Data Description == | == Data Description == | ||
Revision as of 21:23, 28 February 2020
Contents
Overview
The growing menace of Terrorism has been a major issue in many parts of the world. An attack of terrorism in a country impacts its economy and the livelihood of the people impacted by the attack tremendously. The global death toll from terrorism over the past decade has increased by almost 5 times. There has been an average of over 21,000 people getting killed every year worldwide because of terrorism. Because of the growing concern of terrorism across the world in the past decade, we decided to choose this topic for the project. The scope of this project is to understand how the landscape of terrorism and various terrorist activities varies across the world and has changed over time.
Project Motivation
With the recent emergence of Covid virus, containing the epidemic requires an understanding of how corona virus spreads, and factors impacting the intensity of cases within and across regions. This project aims at delivering an R shiny app that first provides a basic understanding of the nature of the virus, e.g. the types of pathogens identified in MERS, the kinds of organisms which are susceptible to MERS contraction, and time series analysis to visualize the evolution of MERS outbreak across a 6-years time period (2012-2018). The detailed description of these variables are shown in the Section:Data Description. Geospatio-temporal analysis will be performed to identify the intensity of outbreak in different regions across time. Finally, we will further deep-dive into how certain factors intensifies the spread of the disease using spatial-join analysis.
Proposed Analytical Methods & Visualisation
1. Exploratory Radar Chart on Pathogen Types
Line Chart (Trellis): To visualise the number of MERS cases over time The line chart will be partitioned by organism_type variable (records the type of organism on which MERS was tested positive)
Slope Chart Compares the ranking/ intensity of MERS incidents in each region over time. Reference: https://www.r-bloggers.com/creating-slopegraphs-with-r/
2. Spatio-Temporal Analysis (Describe point pattern analysis here) Kernel Density Plot 3D Chart (latitude, longitude and time)
3. Spatial Join Analysis Visualisations TBC
Project Timeline
Data Description
The database used for this project is derived from Global Terrorism Database (GTD) handled by the University of Maryland. The database is very comprehensive and includes the repository of terrorist activities starting from 1970 to 2015. For the purposes of this project, the dataset has been filtered to the years 2012 to 2015. Some of the important variables that have been taken into account for this analysis is as mentioned below:
| Data Fields | Description | Example | Datatype |
|---|---|---|---|
| GTD ID | Incidents from the GTD follow a 12‐digit Event ID system, wherein first 8 numbers are for the date recorded and last 4 numbers for sequential case number for the given day (0001, 0002 etc). | 199307250001 | Numeric |
| iyear, imonth, iday | These fields contain the dates and hence will be merged to get the date field. | 2011-02-03 | Numeric |
| country | This field identifies the country or location where the incident occurred. | Afghanistan | Categorical |
| region | This field identifies the region in which the incident occurred. | North America | Categorical |
| latitude | This field records the latitude. | 30.209423 | Numeric |
| longitude | This field records the longitude. | 67.018009 | Numeric |
| attacktype1 | This field captures the general method of attack and often reflects the broad class of tactics used. | Assassination | Categorical |
| weaptype1 | This field records the general type of weapon used in the incident. | Biological | Categorical |
| targtype1 | The target/victim type field captures the general type of target/victim. | Business | Categorical |
| gname | This field contains the name of the group that carried out the attack. | Al-Shabaab | Text variable |
| nkill | This field stores the number of total confirmed fatalities for the incident. | 4 | Numeric |
Software Tools
- RStudio: https://rstudio.com/
Proposed R Packages
| Packages | Purpose |
|---|---|
| plotly() | To help with creating visuals for exploratory analysis |
| ggplot2() | To create elegant data visualizations using grammar of graphics |
| trelliscope() | To create interactive trelliscope displays |
| tidyverse() | To do data manipulation and exploration with dplyr() etc |
| gganimate() | To create plots with animation |
| leaflet() | To create maps within the application |
| spatstat() | To analyse spatial data |
| ads() | To analyse geographical data for spatial point pattern analysis |
| GeoXB() | To create interactive spatial exploratory data analysis |
| Shiny() | To create interactive web application for the final product |
References
Team Members
- Oishee Bhattacharyya
- Jaideep Ballani
- Denise Adele Chua Hui Shan
